Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram

M. Febrian Rachmadi; M. Anwar Ma'sum; I. Made Agus Setiawan; Wisnu Jatmiko

Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram

M. Febrian Rachmadi, M. Anwar Ma'sum, I. Made Agus Setiawan, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

Automatic heart beats classification has attracted much interest for research recently and we are interested to determine the type of arrhythmia from electrocardiogram (ECG) signal automatically. This paper will discuss thoroughly about study and implementation of FLVQ-PSO, an extension from FLVQ algorithm which use MSA and PSO method, and FN-GLVQ, an extension from GLVQ algorithm which use fuzzy logic concept, to classify ECG signals. By using 10-Fold Cross Validation, the algorithm produced an average accuracy 84.02%, 98.25%, 99.00%, and 97.70%, respectively for FLVQ, FLVQ-PSO, GLVQ, and FN-GLVQ.

Original language	English
Title of host publication	2012 Proceedings of SICE Annual Conference, SICE 2012
Publisher	Society of Instrument and Control Engineers (SICE)
Pages	465-470
Number of pages	6
ISBN (Print)	9781467322591
Publication status	Published - 2012
Event	2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012 - Akita, Japan Duration: 20 Aug 2012 → 23 Aug 2012

Publication series

Name	Proceedings of the SICE Annual Conference

Conference

Conference	2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012
Country/Territory	Japan
City	Akita
Period	20/08/12 → 23/08/12

Keywords

Arrhythmia Classification
Biomedical Signal Processing
FLVQ
FLVQ-PSO
FN-GLVQ
GLVQ

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Rachmadi, M. F., Ma'sum, M. A., Setiawan, I. M. A., & Jatmiko, W. (2012). Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram. In 2012 Proceedings of SICE Annual Conference, SICE 2012 (pp. 465-470). Article 6318484 (Proceedings of the SICE Annual Conference). Society of Instrument and Control Engineers (SICE).

Rachmadi, M. Febrian ; Ma'sum, M. Anwar ; Setiawan, I. Made Agus et al. / Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram. 2012 Proceedings of SICE Annual Conference, SICE 2012. Society of Instrument and Control Engineers (SICE), 2012. pp. 465-470 (Proceedings of the SICE Annual Conference).

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title = "Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram",

abstract = "Automatic heart beats classification has attracted much interest for research recently and we are interested to determine the type of arrhythmia from electrocardiogram (ECG) signal automatically. This paper will discuss thoroughly about study and implementation of FLVQ-PSO, an extension from FLVQ algorithm which use MSA and PSO method, and FN-GLVQ, an extension from GLVQ algorithm which use fuzzy logic concept, to classify ECG signals. By using 10-Fold Cross Validation, the algorithm produced an average accuracy 84.02%, 98.25%, 99.00%, and 97.70%, respectively for FLVQ, FLVQ-PSO, GLVQ, and FN-GLVQ.",

keywords = "Arrhythmia Classification, Biomedical Signal Processing, FLVQ, FLVQ-PSO, FN-GLVQ, GLVQ",

author = "Rachmadi, {M. Febrian} and Ma'sum, {M. Anwar} and Setiawan, {I. Made Agus} and Wisnu Jatmiko",

year = "2012",

language = "English",

isbn = "9781467322591",

series = "Proceedings of the SICE Annual Conference",

publisher = "Society of Instrument and Control Engineers (SICE)",

pages = "465--470",

booktitle = "2012 Proceedings of SICE Annual Conference, SICE 2012",

note = "2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012 ; Conference date: 20-08-2012 Through 23-08-2012",

}

Rachmadi, MF, Ma'sum, MA, Setiawan, IMA & Jatmiko, W 2012, Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram. in 2012 Proceedings of SICE Annual Conference, SICE 2012., 6318484, Proceedings of the SICE Annual Conference, Society of Instrument and Control Engineers (SICE), pp. 465-470, 2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012, Akita, Japan, 20/08/12.

Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram. / Rachmadi, M. Febrian; Ma'sum, M. Anwar; Setiawan, I. Made Agus et al.
2012 Proceedings of SICE Annual Conference, SICE 2012. Society of Instrument and Control Engineers (SICE), 2012. p. 465-470 6318484 (Proceedings of the SICE Annual Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Rachmadi, M. Febrian

AU - Ma'sum, M. Anwar

AU - Setiawan, I. Made Agus

AU - Jatmiko, Wisnu

PY - 2012

Y1 - 2012

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AB - Automatic heart beats classification has attracted much interest for research recently and we are interested to determine the type of arrhythmia from electrocardiogram (ECG) signal automatically. This paper will discuss thoroughly about study and implementation of FLVQ-PSO, an extension from FLVQ algorithm which use MSA and PSO method, and FN-GLVQ, an extension from GLVQ algorithm which use fuzzy logic concept, to classify ECG signals. By using 10-Fold Cross Validation, the algorithm produced an average accuracy 84.02%, 98.25%, 99.00%, and 97.70%, respectively for FLVQ, FLVQ-PSO, GLVQ, and FN-GLVQ.

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M3 - Conference contribution

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SN - 9781467322591

T3 - Proceedings of the SICE Annual Conference

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BT - 2012 Proceedings of SICE Annual Conference, SICE 2012

PB - Society of Instrument and Control Engineers (SICE)

T2 - 2012 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan, SICE 2012

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ER -

Rachmadi MF, Ma'sum MA, Setiawan IMA, Jatmiko W. Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram. In 2012 Proceedings of SICE Annual Conference, SICE 2012. Society of Instrument and Control Engineers (SICE). 2012. p. 465-470. 6318484. (Proceedings of the SICE Annual Conference).

Fuzzy Learning Vector Quantization Particle Swarm Optimization (FLVQ-PSO) and Fuzzy Neuro Generalized Learning Vector Quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram

Abstract

Publication series

Conference

Keywords

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Cite this